C4.5: programs for machine learning
C4.5: programs for machine learning
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Measure—based classifier performance evaluation
Pattern Recognition Letters - Special issue on pattern recognition in practice VI
Machine Learning
Thumbs up?: sentiment classification using machine learning techniques
EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
Learning from communication data: language in electronic business negotiations
Learning from communication data: language in electronic business negotiations
WMHAS model for improvement document classification
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
On biases in estimating multi-valued attributes
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Web mining technique framework for intelligent e-business applications
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Web metrics for digital influence measurement
DIWEB'08 Proceedings of the 8th WSEAS international conference on Distance learning and web engineering
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The Web Mining applications have need to be improved with the specific algorithms for the document classification. This paper emphasizes the importance of using appropriate measures and methods for the evaluate of the Web document classification performance. We focus on methods that evaluate how well a classifier performs. The effect of transformations on the confusion matrix are considered for eleven well-known and recently introduced classification measures. We analyze the measure's ability to retain its value under changes in a confusion matrix. We discuss benefits from the use of the invariant and non-invariant measures with respect to characteristics of data classes.